A method and related device for carbon synergy optimization

Abstract

This application relates to the field of energy and carbon optimization technology, and provides a method and related equipment for coordinated energy and carbon optimization. The method includes: acquiring energy and carbon state data for each system node in a target energy system; identifying the equipment response characteristics of each system node based on all energy and carbon state data; generating a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system through constraint analysis based on all equipment response characteristics; and performing coordinated energy and carbon optimization on the target energy system according to the scheduling strategy parameter matrix and the control strategy switching mechanism. This method can improve the accuracy of coordinated energy and carbon optimization.

Description

Technical Field

[0001] This application relates to the field of energy and carbon optimization technology, and in particular to a method and related equipment for energy and carbon synergistic optimization. Background Technology

[0002] Energy system optimization and carbon emission management have become crucial objectives for industrial, urban, and park operations. Existing energy and carbon management technologies typically rely on fixed models, rule-based decision-making, and manual analysis, primarily monitoring, statistically analyzing, and optimizing energy consumption and carbon emission levels. However, in the context of multi-source heterogeneous energy systems and complex carbon emission targets, traditional systems struggle to achieve dynamic coordination and continuous optimization. In practice, while systems can predict energy consumption, schedule equipment, and analyze carbon emissions through models, the operating environment, market electricity prices, carbon trading policies, and production demands often change over time. This makes energy and carbon strategies generated at a particular point in time difficult to adapt to subsequent situations, leading to strategy lag, resource waste, and carbon emission exceedances. These problems are not due to insufficient algorithms or computing power, but rather a misalignment of cognitive models in the strategy formulation and execution phases: strategy generation is based on current static assumptions and point-in-time intentions, while implementation and adjustments depend on future real-world scenarios and target changes. The lack of semantic connection and adaptive extrapolation capabilities between the two results in poor accuracy in energy and carbon co-optimization. Summary of the Invention

[0003] This application provides an energy-carbon synergistic optimization method and related equipment, which can solve the problem of poor accuracy in energy-carbon synergistic optimization.

[0004] In a first aspect, embodiments of this application provide an energy-carbon synergistic optimization method, the method comprising:

[0005] Acquire energy and carbon status data for each system node in the target energy system;

[0006] Identify the device response characteristics of each system node based on all energy and carbon state data;

[0007] Based on the response characteristics of all devices, a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system are generated through constraint analysis. The scheduling strategy parameter matrix is ​​used to describe the control parameters of each system node, and the control strategy switching mechanism is used to describe the event triggering conditions for optimization of each system node.

[0008] Energy and carbon synergistic optimization of the target energy system is performed based on the scheduling strategy parameter matrix and the control strategy switching mechanism.

[0009] Optionally, device response characteristics for each system node can be identified based on all energy and carbon state data, including:

[0010] Multiple key nodes were identified from all system nodes;

[0011] For each critical node, the load transition sensitivity value and energy storage regulation capacity value of the critical node are calculated based on the energy and carbon status data of the critical node, and the load transition sensitivity value and energy storage regulation capacity value are combined as the equipment response characteristics of the system node corresponding to the critical node.

[0012] The preset device response characteristics are used as the device response characteristics for each system node other than the critical node.

[0013] Optionally, multiple key nodes can be identified from all system nodes, including:

[0014] For each system node, perform the following steps:

[0015] Calculate the transmission capacity utilization rate, maximum load carrying time and carbon emission accumulation pressure of the system nodes, and calculate the node change rate, energy storage state change and load change frequency of the system nodes.

[0016] Determine whether the transmission capacity utilization rate, maximum load carrying time and carbon emission accumulation pressure of the system nodes meet the energy storage limit conditions, or whether the node change rate, energy storage status change and load change frequency of the system nodes meet the sensitivity conditions.

[0017] If so, the system node will be treated as a critical node.

[0018] Optionally, based on the response characteristics of all devices, a scheduling strategy parameter matrix and control strategy switching mechanism for the target energy system are generated through constraint analysis, including:

[0019] Construct constraints for energy-carbon synergistic optimization;

[0020] For each system node, a control intention parameter vector is generated based on the system node's energy and carbon state data, equipment response characteristics, and constraints.

[0021] All the control intention parameter vectors are integrated into a matrix to obtain the control intention parameter matrix;

[0022] The constraints are decomposed based on the control intention parameter matrix to obtain multiple sub-constraints, and a scheduling strategy parameter matrix is ​​generated based on all sub-constraints.

[0023] Calculate the dynamic threshold matrix and build an event triggering rule base based on the dynamic threshold matrix; the dynamic threshold matrix includes the threshold of the energy and carbon state data corresponding to each system node;

[0024] By integrating the dynamic threshold matrix and the event-triggered rule base, a control strategy switching mechanism is obtained.

[0025] Optionally, based on the energy and carbon state data of the system nodes, equipment response characteristics, and constraints, a vector of control intent parameters for the system nodes is generated, including:

[0026] Statistical features are extracted from the energy and carbon state data of system nodes to obtain quantitative indicators;

[0027] Convert the device response characteristics into adjustment tendency parameters;

[0028] Transform constraints into constraint penalty parameters;

[0029] By integrating quantitative indicators, adjustment tendency parameters, and constraint penalty parameters into a single vector, the control intention parameter vector of the system node is obtained.

[0030] Optionally, the constraints can be decomposed based on the control intention parameter matrix to obtain multiple sub-constraints, including:

[0031] Based on the control intention parameter vector corresponding to each system node in the control intention parameter matrix, the parameters in the constraint conditions are decomposed and assigned values ​​to obtain the sub-constraint conditions corresponding to each system node.

[0032] Optionally, an event triggering rule base is constructed based on a dynamic threshold matrix, including:

[0033] Based on the dynamic threshold matrix, at least one event triggering rule is constructed for each system node, and all event triggering rules are integrated to obtain an event triggering rule library.

[0034] Secondly, embodiments of this application provide an energy-carbon synergistic optimization device, comprising:

[0035] The acquisition module is used to acquire the energy and carbon status data of each system node in the target energy system;

[0036] The identification module is used to identify the device response characteristics of each system node based on all energy and carbon state data;

[0037] The generation module is used to generate the scheduling strategy parameter matrix and control strategy switching mechanism of the target energy system based on the response characteristics of all devices through constraint analysis. The scheduling strategy parameter matrix is ​​used to describe the control parameters of each system node, and the control strategy switching mechanism is used to describe the event triggering conditions for optimization of each system node.

[0038] The optimization module is used to perform energy-carbon co-optimization of the target energy system based on the scheduling strategy parameter matrix and the control strategy switching mechanism.

[0039] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned energy-carbon synergistic optimization method.

[0040] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned energy-carbon synergistic optimization method.

[0041] The above-mentioned solution in this application has the following beneficial effects:

[0042] In the embodiments of this application, energy and carbon status data of each system node in the target energy system is acquired. Then, based on all energy and carbon status data, the equipment response characteristics of each system node are identified. Based on these equipment response characteristics, a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system are generated through constraint analysis. Finally, energy and carbon co-optimization of the target energy system is performed based on the scheduling strategy parameter matrix and the control strategy switching mechanism. Identifying the equipment response characteristics of system nodes allows for the analysis and deduction of the equipment status of the system nodes themselves, improving the practicality and accuracy of the generated scheduling strategy parameter matrix and control strategy switching mechanism. Performing energy and carbon co-optimization based on a highly accurate scheduling strategy parameter matrix effectively improves the accuracy of energy and carbon co-optimization. Utilizing the control strategy switching mechanism for energy and carbon co-optimization enables adaptation to various scenarios, further enhancing the accuracy of energy and carbon co-optimization.

[0043] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 An energy-carbon synergistic optimization method provided in one embodiment of this application;

[0046] Figure 2 This is a schematic diagram of the structure of an energy-carbon synergistic optimization device provided in an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0048] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0049] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0050] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0051] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0052] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0053] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0054] To address the issue of poor accuracy in existing energy-carbon co-optimization methods, this application provides an energy-carbon co-optimization method. This method acquires energy-carbon state data for each system node in a target energy system, then identifies the equipment response characteristics of each system node based on all energy-carbon state data. Next, based on these equipment response characteristics, constraint analysis is used to generate a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system. Finally, energy-carbon co-optimization is performed on the target energy system according to the scheduling strategy parameter matrix and the control strategy switching mechanism. Identifying the equipment response characteristics of system nodes allows for analysis and deduction of the equipment state of the system nodes themselves, improving the practicality and accuracy of the generated scheduling strategy parameter matrix and control strategy switching mechanism. Performing energy-carbon co-optimization based on a highly accurate scheduling strategy parameter matrix effectively improves the accuracy of energy-carbon co-optimization. Utilizing the control strategy switching mechanism for energy-carbon co-optimization enables adaptation to various scenarios, further enhancing the accuracy of energy-carbon co-optimization.

[0055] The energy-carbon synergistic optimization method provided in this application will be illustrated below.

[0056] like Figure 1 As shown, the energy-carbon synergistic optimization method provided in this application includes the following steps:

[0057] Step 11: Obtain the carbon status data of each system node in the target energy system.

[0058] The aforementioned target energy system refers to the energy system that requires energy-carbon co-optimization, such as urban power grid systems. The aforementioned system nodes refer to the equipment in the target energy system, such as generator sets, battery packs, charging pile clusters, carbon emission sensor arrays, etc. The aforementioned energy-carbon status data includes the operating parameters of the system nodes over a historical period, carbon emission monitoring data, market electricity prices, carbon quota progress, and environmental disturbance indicators, as well as other data related to the energy status and carbon emission status of the system nodes.

[0059] In some embodiments of this application, energy and carbon status data of system nodes can be obtained by accessing the control platform of the target energy system, etc.

[0060] For example, after acquiring energy and carbon state data, it can be processed by normalization, encoding, etc.

[0061] For example, by deploying measuring devices at various nodes of the energy system, including generators, battery energy storage units, load node power meters, and carbon emission sensors, the operating data of each device is continuously collected. Generators and energy storage units output power data in real time, and current, voltage, and power changes are recorded at a sampling frequency of seconds. The power of the load node is sampled through the distribution port, and combined with the load metering module to generate instantaneous power and cumulative energy consumption data of the node. Carbon emission sensors continuously monitor each emission source, collecting emission amount, emission rate, and cumulative emission amount, and verifying the zero-point drift and range linearity of the sensor during the collection process.

[0062] Record market electricity price signals, carbon quota consumption progress, and environmental disturbance indicators, including temperature, humidity, and wind speed.

[0063] Electricity price signals and carbon quota consumption data are obtained from the energy market and carbon trading platform. The data interface collects current market electricity prices, electricity price change trends, carbon quota remaining amount, and quota consumption rate at a minute-level update frequency. Environmental disturbance indicators are collected through a network of meteorological sensors deployed on-site, including parameters such as temperature, humidity, and wind speed. Local microclimate disturbances are also obtained through accelerometers and wind vanes. This environmental data is synchronized with the system operation data, labeled with timestamps, and stored in a unified database.

[0064] The collected data is aligned according to timestamps, and an initial energy carbon state feature set is generated based on scenario-triggered encoding.

[0065] The collected energy, carbon emission, and environmental data are time-series aligned. Data from different sources and with different sampling frequencies are unified onto a unified time grid through interpolation and synchronization algorithms. Linear interpolation, moving average, and anomaly marking are used to handle data anomalies and missing values ​​to ensure data continuity. Subsequently, the status of each node is encoded according to the energy operation scenario and key event triggers, such as power range encoding, energy storage charging and discharging status encoding, carbon emission level encoding, and environmental disturbance identification encoding. The encoding results generate an initial energy and carbon state feature set containing the status information, constraints, and time series characteristics of each node.

[0066] Step 12: Identify the device response characteristics of each system node based on all energy and carbon state data.

[0067] The above-mentioned equipment response characteristics are used to describe the response state of system nodes, including load transition sensitivity value and energy storage regulation capability value. The load transition sensitivity value is used to describe the degree of load change of the equipment when it is disturbed. The larger the value, the greater the degree of load change. The energy storage regulation capability value is used to describe the ability of the equipment to adjust the energy throughput state. The larger the value, the stronger the ability of the equipment to adjust the energy throughput state.

[0068] In some embodiments of this application, the step of identifying the device response characteristics of each system node based on all energy and carbon state data includes:

[0069] The first step is to identify several key nodes from all system nodes.

[0070] Specifically, for each system node, the following steps are performed:

[0071] First, calculate the transmission capacity utilization rate, maximum load carrying time and carbon emission accumulation pressure of the system nodes, and calculate the node change rate, energy storage state change and load change frequency of the system nodes.

[0072] Then, determine whether the transmission capacity utilization rate, maximum load carrying time and carbon emission accumulation pressure of the system nodes meet the energy storage limit conditions, or whether the node change rate, energy storage state change and load change frequency of the system nodes meet the sensitivity conditions.

[0073] If so, the system node will be treated as a critical node.

[0074] Otherwise, the system nodes are not marked.

[0075] For example, the ratio of the actual transmission volume to the rated capacity of the device corresponding to the matrix node is used as the capacity utilization rate, and the duration of the device's load relative to the preset load is used as the maximum load carrying time. The carbon emissions of the device over a period of time are accumulated to obtain the cumulative carbon emission pressure. The rate of change of the load or carbon emissions of the device over a period of time is calculated to obtain the node change rate. Data that can measure the change of the device's energy storage, such as the change in the device's energy storage, can be used as the change in energy storage status. The number of times the load change of the device exceeds the preset change amount over a period of time is counted, and the number and duration are substituted into the frequency calculation formula to calculate the load change frequency.

[0076] It should be noted that the above-mentioned energy storage limitations can be: transmission capacity utilization, maximum load carrying time, and cumulative carbon emission pressure all exceeding the corresponding preset thresholds. The above-mentioned sensitivity conditions can be: node change rate, energy storage state change, and load abrupt change frequency all exceeding the corresponding preset thresholds.

[0077] Alternatively, a matrix node can be determined as a critical node by calculating the flow rate percentage (or carbon emission percentage), peak load frequency, and energy storage regulation capacity, and by determining whether these parameters all exceed corresponding preset thresholds. The node's flow rate percentage or carbon emission percentage is obtained by dividing the node's total flow rate or carbon emission by the total flow rate or emissions of the entire network. The peak load frequency is obtained by setting a high load threshold for the node's load or energy flow sequence and statistically analyzing the proportion of times or durations exceeding the threshold. The energy storage regulation capacity is then analyzed by combining the node's capacity and the charge / discharge rate of the connected energy storage device with the analysis of its regulation range under load fluctuations.

[0078] The second step involves calculating the load transition sensitivity value and energy storage regulation capacity value of each key node based on its energy and carbon status data, and then combining these values ​​to determine the equipment response characteristics of the corresponding system node.

[0079] For example, load transition sensitivity can be quantified by load change rate, abrupt change frequency, etc. Nodes with large change amplitude, fast load fluctuation, carbon accumulation close to the upper limit, and low energy storage regulation capacity have high load transition sensitivity. Nodes with high sensitivity indicate that they respond strongly to disturbances and are key nodes that may cause system instability or carbon emission exceedance.

[0080] Energy storage regulation capability can be quantified by the energy storage capacity and charge / discharge rate of a node. Nodes with large energy storage capacity and high charge / discharge rate have a large energy storage regulation capability.

[0081] The third step is to use the preset device response characteristics as the device response characteristics for each system node other than the critical node.

[0082] For example, the initial load transition sensitivity value and the initial energy storage regulation capacity value are preset to form the preset equipment response characteristics.

[0083] Step 13: Based on the response characteristics of all devices, generate the scheduling strategy parameter matrix and control strategy switching mechanism of the target energy system through constraint analysis.

[0084] The aforementioned scheduling strategy parameter matrix is ​​used to describe the control parameters of each system node, and the control strategy switching mechanism is used to describe the event triggering conditions optimized for each system node. The optimized events include: triggering the control behaviors of system nodes such as carbon emission reduction, energy storage compensation, power adjustment, and load switching.

[0085] In some embodiments of this application, the steps of generating the scheduling strategy parameter matrix and control strategy switching mechanism of the target energy system based on the response characteristics of all devices through constraint analysis include:

[0086] The first step is to construct constraints for energy-carbon synergistic optimization.

[0087] Specifically, constraints are used to describe the physical parameter constraints that need to be followed in the energy-carbon co-optimization process, including: capacity limits, carbon emission quotas, etc.

[0088] For example, the constraints are as follows:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] in, For nodes In time The output power, For nodes In time The energy storage state, These are the energy storage charging and discharging efficiencies, To constrain carbon emissions, , Represents the set of system nodes. Represents the set of scheduling times. Represents a node In time Total load demand, Represents a node Maximum output power Represents a node In time The energy storage state, Represents a node In time The charging power, Represents a node In time The discharge power, Indicates the scheduling policy parameters The system ensures that carbon emissions are within acceptable limits during scheduling, while also meeting energy supply and demand balance, energy storage capacity constraints, and carbon emission ceiling requirements.

[0094] The second step involves generating a control intention parameter vector for each system node based on its energy and carbon state data, equipment response characteristics, and constraints.

[0095] Specifically, statistical features are extracted from the energy and carbon state data of system nodes to obtain quantitative indicators; equipment response characteristics are transformed into regulation tendency parameters; constraints are transformed into constraint penalty parameters; and quantitative indicators, regulation tendency parameters, and constraint penalty parameters are integrated into a vector to obtain the regulation intention parameter vector of system nodes.

[0096] For example, energy and carbon state data are processed through normalization, differencing, or statistical feature extraction to obtain quantitative indicators; equipment response characteristics are transformed into regulation tendency parameters through scaling or weighting; and parameters in the constraints are transformed into constraint penalty parameters through proportionalization or constraint strength indicators. Then, the quantitative indicators, regulation tendency parameters, and constraint penalty parameters are integrated into a vector to obtain the system node's control intention parameter vector.

[0097] The third step is to integrate all the control intention parameter vectors into a matrix to obtain the control intention parameter matrix.

[0098] The fourth step is to decompose the constraints based on the control intention parameter matrix to obtain multiple sub-constraints, and then generate a scheduling strategy parameter matrix based on all the sub-constraints.

[0099] Specifically, based on the control intention parameter vector corresponding to each system node in the control intention parameter matrix, the parameters in the constraints are decomposed and assigned values ​​to obtain the sub-constraints corresponding to each system node, and the scheduling strategy parameter matrix is ​​generated based on all the sub-constraints.

[0100] For example, the scheduling strategy parameters can be obtained by normalizing the parameters in the sub-constraints, and these parameters can be integrated into a matrix to obtain the scheduling strategy parameter matrix.

[0101] For example, when global constraints are broken down into node-level sub-constraints, the total load balance is first allocated according to the rated capacity ratio of each node or node group based on the system topology and node functions, generating upper and lower power limits for each node; the total carbon emission limit is allocated according to the node's carbon emission capacity or historical emission ratio, forming the node's carbon accumulation upper limit; the capacity limit of critical nodes is directly mapped to the node's own maximum load constraint; for example, in a microgrid containing three types of nodes (generation nodes, energy storage nodes, and load nodes), the total load is 3000kW, the total carbon emission limit is 500kg of carbon dioxide, and the generation node... With a rated capacity ratio of 60%, energy storage nodes accounting for 25%, and load nodes accounting for 15%, the upper and lower power limits can be set to 1800kW, 750kW, and 450kW, respectively. At the same time, upper limits for charge and discharge rates and energy storage capacity constraints are set for energy storage nodes, and switching level constraints are set for load nodes. Corresponding carbon accumulation upper limits are allocated according to the carbon emission capacity of the nodes, thus forming a set of directly executable node-level constraints. For each sub-constraint, the upper and lower power limits, upper limits for energy storage charge and discharge rates, load switching conditions, and carbon emission accumulation limits are clearly defined, forming a set of directly executable node-level constraints.

[0102] The fifth step is to calculate the dynamic threshold matrix and build an event triggering rule base based on the dynamic threshold matrix.

[0103] The dynamic threshold matrix mentioned above includes thresholds for the energy and carbon state data corresponding to each system node.

[0104] Specifically, based on the dynamic threshold matrix, at least one event triggering rule is constructed for each system node, and all event triggering rules are integrated to obtain an event triggering rule library.

[0105] For example, based on the energy and carbon status data of system nodes, thresholds are set for the corresponding nodes, such as maximum power deviation, load switching threshold, and carbon emission critical value. The maximum power deviation is determined by the difference between the peak and valley power values ​​in the historical operating sequence of the node, combined with the node's response delay and energy storage regulation capability, to determine the allowable power fluctuation range in the current period. The load switching threshold is set based on the historical load fluctuation amplitude and the node's load switching response characteristics to ensure that switching does not lead to system instability. The carbon emission critical value is proportionally allocated based on the node's cumulative carbon emissions and carbon quota usage progress to obtain the maximum allowable carbon emission level in the current period, thereby quantifying the node's operable boundaries in terms of power, load, and carbon emissions.

[0106] Multiple events for energy-carbon co-optimization are constructed to determine control strategies for system nodes (which can be obtained using models such as reinforcement learning). These events are then combined with dynamic thresholds to construct event triggering rules. For example, when a node load exceeds a preset high-load threshold or the available energy storage capacity falls below a safe lower limit, a power adjustment or load switching strategy is triggered; when a node's accumulated carbon emissions approach its allocated quota, a carbon reduction or energy storage compensation strategy is triggered. By quantifying these conditions into thresholds or state indicators, dynamic switching of node control strategies is achieved, thereby ensuring that both constraints are met and energy flow and carbon emission management are optimized under different operating conditions.

[0107] The sixth step is to integrate the dynamic threshold matrix and the event triggering rule base to obtain the control strategy switching mechanism.

[0108] Specifically, the dynamic threshold matrix and event triggering rule base are integrated into a single data set to obtain a control strategy switching mechanism.

[0109] For example, threshold triggering conditions for different nodes are differentiated based on their functions, capacity, and operating characteristics. For instance, a power generation node might have a power over-limit threshold set at 90% of its rated capacity, an energy storage node at an abnormal charge / discharge rate threshold at 80% of its maximum charge / discharge capacity, and a load node at a load fluctuation threshold of 1.5 times the historical average load fluctuation. Simultaneously, carbon emission thresholds can be adjusted based on the node's cumulative emissions as a percentage of its quota. For example, a threshold for a heavy-carbon-emission industrial load node might be 20% of its remaining quota, while for a low-carbon load node it could be set to 10%. This differentiated threshold configuration enables refined monitoring and control of different nodes. Logical relationships between nodes are established to ensure that when an event is triggered by a node, the operating status of affected adjacent or related nodes can be detected. The rule base stores event conditions and corresponding strategy switching logic in tabular or vectorized form, recording event types, trigger thresholds, and response actions.

[0110] The system monitors all key nodes in real time. When the node's operating parameters reach or exceed the dynamic threshold, the system determines the event type and scope of impact based on the event triggering rules. The logic includes condition combination judgment. For example, when load transition and energy storage constraint anomaly occur simultaneously, the system determines the order of triggering strategies according to priority. The strategy switching logic includes adjusting the execution priority, updating the power allocation range and switching conditions, so that the strategy switching between different nodes can be coordinated.

[0111] The threshold set and event triggering rules are integrated into a policy switching mechanism; the dynamically calculated thresholds and event triggering rule base are merged to form a complete policy switching mechanism. This mechanism, as a scheduling system module, can receive node status data in real time, determine triggering conditions, and automatically select or update the corresponding policy parameter matrix.

[0112] The generative policy function is defined as follows:

[0113] ;

[0114] in, For nodes In time The state vector includes load, electricity price, energy storage capacity, and renewable energy output; This indicates executable scheduling actions, such as power allocation and energy storage charging / discharging strategies. The node reward function comprehensively considers energy conservation, carbon emission reduction, and operational safety. This strategy generation formula demonstrates that the intelligent agent autonomously generates the optimal scheduling strategy by maximizing the expected reward.

[0115] Step 14: Perform energy-carbon co-optimization on the target energy system based on the scheduling strategy parameter matrix and the control strategy switching mechanism.

[0116] Specifically, for each system node in the target energy system, the current energy and carbon status data of the system node is analyzed to determine whether it triggers a control strategy switching mechanism. If so, the switched control strategy is executed, and the parameters corresponding to this control strategy in the scheduling strategy parameter matrix are used as the target parameters for control, thereby achieving energy and carbon co-optimization of the system node. This process is repeated for each node in the target energy system to achieve energy and carbon co-optimization of the target energy system.

[0117] For example, the scheduling strategy parameter matrix is ​​distributed to each energy device and the scheduling control system;

[0118] The generated scheduling strategy parameter matrix is ​​transmitted to the control units of each energy device and the central dispatch control system through the communication network. The transmission adopts a reliable industrial communication protocol to ensure the integrity and timeliness of the strategy parameters. The strategy matrix includes the node power limit, load switching conditions, execution priority and power allocation range. After receiving the matrix, the device caches it locally and interfaces with the real-time operation control program.

[0119] Real-time monitoring of key nodes, including energy storage status, load node power, and carbon emission sensor readings;

[0120] Sensors and intelligent controllers are used to collect real-time data on the charging and discharging status of the energy storage system, the instantaneous power output of load nodes, and emissions data from carbon emission monitoring nodes. The data collection frequency is set according to the node characteristics, typically on the order of seconds or minutes. The collected data is timestamped to form a continuous sequence of node statuses.

[0121] Based on the strategy switching mechanism, it can determine in real time whether the running status of key nodes deviates from the threshold and mark abnormal or critical situations.

[0122] The system compares and analyzes the collected real-time node data with the dynamic thresholds in the strategy matrix, including power limits, load switching points, and carbon emission constraints. Through logical judgment and condition combination algorithms, the system determines whether a node is in a normal, critical, or abnormal state. When a node's state exceeds the threshold, it is marked in the node state record, and the event information and trigger time are recorded in the energy and carbon trajectory database.

[0123] The process of forming the energy carbon trajectory dataset is as follows:

[0124] During the policy issuance and execution process, the operational status of key nodes is monitored in real time based on the policy switching mechanism, including energy storage status, load node power, carbon emission sensor readings, and equipment response delay.

[0125] During the strategy issuance and execution process, the system continuously monitors the operating status of key nodes through the data acquisition module. The data acquisition objects include the charging and discharging power and status of energy storage devices, the instantaneous power output of load nodes, the emission data of carbon emission sensors, and the equipment response delay. The data acquisition frequency is set according to the equipment characteristics, generally at the second or minute level. The acquired data is aligned with timestamps and uploaded to the central data processing unit in real time to provide basic raw data for trajectory generation.

[0126] Record the state changes of each node in time series, and mark the node state adjustment events triggered by strategy switching;

[0127] The collected operational data of each node is recorded sequentially in time series to form a continuous state change sequence. At the same time, the system detects policy adjustment events triggered by load changes, energy storage constraints, or carbon quota pressures based on the policy switching mechanism. Each policy trigger event is marked in the trajectory, and the triggering node, time, and adjustment parameters are recorded to ensure the complete correspondence between policy execution and node state changes in the energy and carbon trajectory data.

[0128] The collected data is integrated in a time sequence and anomaly flagged.

[0129] The data collected in time series is integrated, and the power, energy storage status and carbon emission information of each node are unified on the same time axis to solve the problem of sampling interval difference. For outliers in the data, such as short-term sensor failure or measurement noise, correction or labeling is carried out through missing value filling, smoothing filtering and anomaly identification to ensure the continuity and reliability of trajectory data.

[0130] High-dimensional feature encoding is performed on the trajectory to extract the coupling relationship between nodes, dynamic response mode and strategy triggering effect, and generate energy carbon trajectory dataset for cross-cycle regulation judgment and regulation feedback.

[0131] The integrated trajectory data is encoded with high-dimensional features, and the temporal state, energy storage changes, load fluctuations and carbon emission changes of each node are mapped into vector representations. The coupling relationship between nodes is calculated through feature vectors, the dynamic response mode of nodes is analyzed, and the influence of policy switching events is combined to extract the cross-node regulation influence links. The final generated energy and carbon trajectory dataset contains the basis for cross-cycle regulation judgment and regulation feedback information.

[0132] It should be noted that, in order to further improve the accuracy of energy-carbon synergistic optimization, a control feedback matrix can be formed after this step:

[0133] The carbon trajectory dataset is compared with the current scheduling strategy parameter matrix to analyze the deviation between the actual response of each key node and the preset value of the strategy.

[0134] The system will compare the real-time operating data of each key node in the carbon trajectory dataset, including load curves, energy storage status, carbon emission monitoring values ​​and equipment response delays, with the preset power range, load switching conditions and carbon emission constraint thresholds in the current scheduling strategy parameter matrix node by node. The comparison is achieved through time series alignment to ensure that the actual state of each node at the same time step completely corresponds to the strategy setting.

[0135] Identify trajectory deviations and constraint discrepancies, including power overruns, load transition lags, carbon emission pressure deviations, and abnormal energy storage responses;

[0136] By comparing the results, the system detects whether each node has experienced power over-limit, load transition lag, carbon emission pressure deviation, or abnormal energy storage response. Deviation judgment uses threshold discrimination logic to record the state of the node exceeding the upper limit or falling below the lower limit during operation as deviation events, and at the same time, it calculates the deviation magnitude and duration.

[0137] Deviation events are classified and labeled, and the impact of each type of deviation on overall energy and carbon scheduling is assessed by combining node coupling relationships and cross-cycle response characteristics.

[0138] The detected deviation events are classified into power-related, load-related, carbon emission-related, and energy storage response-related categories. Each category of event is labeled with the occurrence node, time period, and amplitude information. Combining the coupling relationship between nodes and the cross-cycle response characteristics, the transmission effect of deviation events on energy flow and carbon emission links is analyzed, and the system-level impact range that deviations may cause is determined, providing a basis for the weight allocation of the feedback matrix.

[0139] Generate a control feedback matrix based on the type and degree of impact of the deviation;

[0140] Based on the type, magnitude, and scope of the deviation, various deviation events are mapped to corresponding entries in the control feedback matrix. Each row in the matrix represents the type of deviation event, each column corresponds to a key node or node group, and the cell value reflects the deviation magnitude and priority level. By integrating cross-node impact, deviation duration, and event category, a complete control feedback matrix is ​​formed.

[0141] In some embodiments of this application, a multi-objective optimization framework is constructed to quantify the effect of energy-carbon synergistic optimization. The multi-objective optimization framework is as follows:

[0142] ;

[0143] in, The target weight coefficient, Total energy cost, For carbon emission costs, This formula reflects the comprehensive trade-off between energy consumption, carbon emissions, and operational safety in multi-objective optimization, representing the risks to system operation.

[0144] It is worth mentioning that identifying the device response characteristics of system nodes can analyze and deduce the device status of the system nodes themselves, improve the practicality and accuracy of the generated scheduling strategy parameter matrix and control strategy switching mechanism, and perform energy-carbon co-optimization based on the highly accurate scheduling strategy parameter matrix, which can effectively improve the accuracy of energy-carbon co-optimization. Utilizing the control strategy switching mechanism for energy-carbon co-optimization enables energy-carbon co-optimization to adapt to various scenarios, further improving the accuracy of energy-carbon co-optimization.

[0145] Furthermore, this application achieves real-time perception of energy system operation status, carbon emission monitoring, market electricity prices, and environmental disturbances through multi-source data fusion and scenario triggering mechanisms, enabling the construction of a comprehensive energy and carbon status feature set. By leveraging topology and time-series analysis and disturbance propagation tracking, it effectively identifies key load transition points, carbon quota pressure nodes, and equipment response bottlenecks, providing quantitative parameters for cross-cycle regulation. Combined with dynamic thresholds and event-driven strategy switching, it achieves adaptive adjustment and immediate response of scheduling strategies. During strategy execution, through key node monitoring, deviation locking, and delay verification, a complete energy and carbon trajectory dataset is formed, providing accurate reference for system operation. Finally, through trajectory comparison and constraint difference analysis, a regulation feedback matrix is ​​generated, continuously updating strategy parameters to achieve cross-cycle closed-loop regulation, improving energy dispatch efficiency, carbon emission control accuracy, and overall system stability, providing a feasible technical guarantee for the dynamic collaborative optimization of complex energy networks.

[0146] The energy-carbon synergistic optimization device provided in this application is described below as an example.

[0147] like Figure 2As shown, this application provides an energy-carbon synergistic optimization device, the energy-carbon synergistic optimization device 200 including:

[0148] The acquisition module 201 is used to acquire the energy and carbon status data of each system node in the target energy system;

[0149] Identification module 202 is used to identify the device response characteristics of each system node based on all energy and carbon state data;

[0150] The generation module 203 is used to generate the scheduling strategy parameter matrix and control strategy switching mechanism of the target energy system based on the response characteristics of all devices through constraint analysis; the scheduling strategy parameter matrix is ​​used to describe the control parameters of each system node, and the control strategy switching mechanism is used to describe the event triggering conditions optimized for each system node;

[0151] The optimization module 204 is used to perform energy-carbon co-optimization on the target energy system based on the scheduling strategy parameter matrix and the control strategy switching mechanism.

[0152] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0153] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0154] like Figure 3 As shown, an embodiment of this application provides a terminal device, wherein the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 3The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0155] Specifically, when the processor D100 executes the computer program D102, it acquires the energy and carbon status data of each system node in the target energy system. Then, based on all the energy and carbon status data, it identifies the equipment response characteristics of each system node. Next, based on all the equipment response characteristics, it generates a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system through constraint analysis. Finally, it performs energy and carbon co-optimization on the target energy system according to the scheduling strategy parameter matrix and the control strategy switching mechanism. Identifying the equipment response characteristics of system nodes allows for the analysis and deduction of the equipment status of the system nodes themselves, improving the practicality and accuracy of the generated scheduling strategy parameter matrix and control strategy switching mechanism. Performing energy and carbon co-optimization based on a highly accurate scheduling strategy parameter matrix effectively improves the accuracy of energy and carbon co-optimization. Utilizing the control strategy switching mechanism for energy and carbon co-optimization enables adaptation to various scenarios, further improving the accuracy of energy and carbon co-optimization.

[0156] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0157] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0158] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0159] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0160] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the energy-carbon co-optimization method apparatus / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0161] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0162] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0163] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention.

Claims

1. A method for coordinated energy and carbon optimization, characterized in that, include: Acquire energy and carbon status data for each system node in the target energy system; Identify the device response characteristics of each system node based on all energy and carbon state data; The device response characteristics are used to describe the response state of system nodes, including load transition sensitivity values ​​and energy storage regulation capacity values. Based on the response characteristics of all devices, a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system are generated through constraint analysis. The scheduling strategy parameter matrix is ​​used to describe the control parameters of each system node, and the control strategy switching mechanism is used to describe the event triggering conditions optimized for each system node. The target energy system is optimized for energy and carbon co-operation based on the scheduling strategy parameter matrix and the control strategy switching mechanism. The step of generating the scheduling strategy parameter matrix and control strategy switching mechanism of the target energy system based on the response characteristics of all devices through constraint analysis includes: Construct constraints for energy-carbon synergistic optimization; For each of the system nodes, a control intention parameter vector is generated based on the energy and carbon state data, equipment response characteristics, and constraints of the system node. All the control intention parameter vectors are integrated into a matrix to obtain the control intention parameter matrix; The constraints are decomposed based on the control intention parameter matrix to obtain multiple sub-constraints, and a scheduling strategy parameter matrix is ​​generated based on all sub-constraints. Calculate a dynamic threshold matrix and construct an event triggering rule base based on the dynamic threshold matrix; the dynamic threshold matrix includes thresholds for the energy and carbon state data corresponding to each system node; By integrating the dynamic threshold matrix and the event triggering rule base, a control strategy switching mechanism is obtained.

2. The energy-carbon synergistic optimization method according to claim 1, characterized in that, The identification of device response characteristics for each system node based on all energy and carbon state data includes: Multiple key nodes were identified from all system nodes; For each critical node, the load transition sensitivity value and energy storage regulation capacity value of the critical node are calculated based on the energy and carbon status data of the critical node, and the load transition sensitivity value and energy storage regulation capacity value are combined as the equipment response characteristics of the system node corresponding to the critical node. The preset device response characteristics are used as the device response characteristics for each system node other than the critical node.

3. The energy-carbon synergistic optimization method according to claim 2, characterized in that, The identification of multiple key nodes from all system nodes includes: For each of the system nodes, the following steps are performed: Calculate the transmission capacity utilization rate, maximum load carrying time and carbon emission accumulation pressure of the system nodes, and calculate the node change rate, energy storage state change and load change frequency of the system nodes; Determine whether the transmission capacity utilization rate, maximum load carrying time and carbon emission accumulation pressure of the system node meet the energy storage limit conditions, or whether the node change rate, energy storage state change and load change frequency of the system node meet the sensitivity conditions. If so, the system node will be considered a critical node.

4. The energy-carbon synergistic optimization method according to claim 1, characterized in that, The generation of the control intention parameter vector for the system node based on the energy and carbon state data of the system node, the equipment response characteristics, and the constraints includes: Statistical features are extracted from the energy and carbon state data of the system nodes to obtain quantitative indicators; The device response characteristics are converted into adjustment tendency parameters; Transform the constraints into constraint penalty parameters; The quantitative indicators, the adjustment tendency parameters, and the constraint penalty parameters are integrated into a vector to obtain the adjustment intention parameter vector of the system node.

5. The energy-carbon synergistic optimization method according to claim 4, characterized in that, The constraint conditions are decomposed based on the control intention parameter matrix to obtain multiple sub-constraint conditions, including: Based on the control intention parameter vector corresponding to each system node in the control intention parameter matrix, the parameters in the constraint conditions are decomposed and assigned values ​​to obtain the sub-constraint conditions corresponding to each system node.

6. The energy-carbon synergistic optimization method according to claim 5, characterized in that, The construction of the event triggering rule base based on the dynamic threshold matrix includes: Based on the dynamic threshold matrix, at least one event triggering rule is constructed for each system node, and all event triggering rules are integrated to obtain an event triggering rule library.

7. An energy-carbon synergistic optimization device, characterized in that, include: The acquisition module is used to acquire the energy and carbon status data of each system node in the target energy system; The identification module is used to identify the device response characteristics of each system node based on all energy and carbon state data; The device response characteristics are used to describe the response state of system nodes, including load transition sensitivity values ​​and energy storage regulation capacity values. The generation module is used to generate a scheduling strategy parameter matrix and a control strategy switching mechanism for the target energy system based on the response characteristics of all devices through constraint analysis. The scheduling strategy parameter matrix is ​​used to describe the control parameters of each system node, and the control strategy switching mechanism is used to describe the event triggering conditions optimized for each system node. The optimization module is used to perform energy-carbon co-optimization on the target energy system based on the scheduling strategy parameter matrix and the control strategy switching mechanism. Specifically, the generation module is used to implement: Construct constraints for energy-carbon synergistic optimization; For each of the system nodes, a control intention parameter vector is generated based on the energy and carbon state data, equipment response characteristics, and constraints of the system node. All the control intention parameter vectors are integrated into a matrix to obtain the control intention parameter matrix; The constraints are decomposed based on the control intention parameter matrix to obtain multiple sub-constraints, and a scheduling strategy parameter matrix is ​​generated based on all sub-constraints. Calculate a dynamic threshold matrix and construct an event triggering rule base based on the dynamic threshold matrix; the dynamic threshold matrix includes thresholds for the energy and carbon state data corresponding to each system node; By integrating the dynamic threshold matrix and the event triggering rule base, a control strategy switching mechanism is obtained.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the energy-carbon synergistic optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the energy-carbon synergistic optimization method as described in any one of claims 1 to 6.

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